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Documentation Index

Fetch the complete documentation index at: https://docs.agno.com/llms.txt

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Continue a Deep Research interaction across turns. Each response carries an interaction_id; the next turn references it via previous_interaction_id so only the new user message is sent on the wire. The server already has the prior research and its citations. Persisting the interaction ID requires a database. The assistant message stores it under provider_data, and the next turn reads it back.

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Code

cookbook/90_models/google/gemini_interactions/deep_research_multi_turn.py
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.google import GeminiInteractions

agent = Agent(
    model=GeminiInteractions(
        agent="deep-research-preview-04-2026",
        thinking_summaries="auto",
    ),
    add_history_to_context=True,
    db=SqliteDb(db_file="tmp/data.db"),
    markdown=True,
)

if __name__ == "__main__":
    agent.print_response(
        "Research the current state of solid-state battery commercialization "
        "and summarize the leading approaches."
    )

    agent.print_response(
        "Dive deeper into the sulfide-electrolyte approach: who the leading "
        "labs and companies are, and what their reported milestones look like."
    )

    agent.print_response(
        "Based on everything we've covered, which approach has the clearest "
        "path to mass-market EV deployment in the next five years?"
    )

​
Usage

1

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate
2

Set your API key

export GOOGLE_API_KEY=xxx
3

Install dependencies

uv pip install -U "google-genai>=2.0" agno
4

Run Agent

python cookbook/90_models/google/gemini_interactions/deep_research_multi_turn.py